Person‐specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels

This paper compares the multilevel modelling (MLM) approach and the person‐specific (PS) modelling approach in examining autoregressive (AR) relations with intensive longitudinal data. Two simulation studies are conducted to examine the influences of sample heterogeneity, time series length, sample size, and distribution of individual level AR coefficients on the accuracy of AR estimates, both at the population level and at the individual level. It is found that MLM generally outperforms the PS approach under two conditions: when the sample has a homogeneous AR pattern, namely, when all individuals in the sample are characterized by AR processes with the same order; and when the sample has heterogeneous AR patterns, but a multilevel model with a sufficiently high order (i.e., an order equal to or higher than the maximum order of individual AR patterns in the sample) is fitted and successfully converges. If a lower‐order multilevel model is chosen for heterogeneous samples, the higher‐order lagged effects are misrepresented, resulting in bias at the population level and larger prediction errors at the individual level. In these cases, the PS approach is preferable, given sufficient measurement occasions (T ≥ 50). In addition, sample size and distribution of individual level AR coefficients do not have a large impact on the results. Implications of these findings on model selection and research design are discussed.     

Bayesian analysis of longitudinal multitrait–multimethod data with ordinal response variables

A new multilevel latent state graded response model for longitudinal multitrait–multimethod (MTMM) measurement designs combining structurally different and interchangeable methods is proposed. The model allows researchers to examine construct validity over time and to study the change and stability of constructs and method effects based on ordinal response variables. We show how Bayesian estimation techniques can address a number of important issues that typically arise in longitudinal multilevel MTMM studies and facilitates the estimation of the model presented. Estimation accuracy and the impact of between‐ and within‐level sample sizes as well as different prior specifications on parameter recovery were investigated in a Monte Carlo simulation study. Findings indicate that the parameters of the model presented can be accurately estimated with Bayesian estimation methods in the case of low convergent validity with as few as 250 clusters and more than two observations within each cluster. The model was applied to well‐being data from a longitudinal MTMM study, assessing the change and stability of life satisfaction and subjective happiness in young adults after high‐school graduation. Guidelines for empirical applications are provided and advantages and limitations of a Bayesian approach to estimating longitudinal multilevel MTMM models are discussed.     

Goal orientation, task difficulty, and task interest: A multilevel analysis

We studied whether goal orientation affects the relationships that task difficulty and interest have with self-set goals. Using a sample of 499 undergraduate students who listed grade goals for their classes, we employed Hierarchical Linear Modeling to explain differences in the extent to which difficulty and interest related to goals. Higher goals were set for more interesting and easier classes, and for individuals higher in mastery goal orientation and for those lower in performance-avoidance orientation (at average levels of interest and difficulty). Furthermore, performance-approach and performance-avoidance orientations reduced and strengthened (respectively) the effects of task difficulty on self-set goals, and the buffering effect of mastery orientation was marginally significant. Portions of this paper were presented at the 19^th Annual Conference of the Society for Industrial and Organizational Psychology, Chicago, IL, in April 2004.     

Multilevel analysis of matching behavior

Multilevel modeling has been considered a promising statistical tool in the field of the experimental analysis of behavior and may serve as a convenient statistical analysis for matching behavior because it structures data in groups (or levels) to account simultaneously for the within‐subject and between‐subject variances. Heretofore, researchers have sometimes pooled data erroneously from different subjects in a single analysis by using average ratios, average response and reinforcer rates, aggregation of subjects, etc. Unfortunately, this leads to loss of information and biased estimations, which can severely undermine generalization of the results. Instead, a multilevel approach is advocated to combine several subjects' matching behavior. A reanalysis of previous data on matching behavior is provided to illustrate the method and point out its advantages. It illustrates that multilevel regression leads to better estimations, is more convenient, and offers more behavioral information. We hope this paper will encourage the use of multilevel modeling in the statistical practices of behavior analysts.     

Individual,situational, and cultural correlates of acquiescent responding: Towards a unified conceptual framework

Acquiescence (‘yea-saying’) can seriously harm the validity of self-report questionnaire data. Towards a better understanding of why some individuals and groups acquiesce more strongly than others do, we developed a unified conceptual framework of acquiescent responding. Our framework posits that acquiescent responding is a joint function of respondent characteristics (e.g. age, education, values), situational/survey characteristics (e.g., interview privacy, respondents’ interest), and cultural characteristics (e.g., social norms, economic development). The framework posits two putative mechanisms through which these characteristics may relate to acquiescence: cognitive processing capacities and deferential communication styles. Multilevel analyses using data from 60 heterogeneous countries from the World Values Survey (N = 90,347) support our framework's proposition that acquiescence is a joint function of respondent, situational, and cultural characteristics. Acquiescence was higher among respondents who were older (over 55 years old), less educated, who valued deference (i.e., conformity and tradition), and, unexpectedly, were male. Interview privacy corresponded to lower acquiescence, but this association was small and vanished after including respondent characteristics. Unexpectedly, acquiescence was higher in interviewees who showed a stronger interest in the interview. Finally, acquiescence was considerably higher in countries with stronger social norms of deference. We discuss implications of these findings for the validity of research based on self-report data and delineate how our framework can guide future inquiries into acquiescent responding.     

Understanding Gang Joining from a Cross Classified Multi-Level Perspective

The present study seeks to address the gap in our understanding of the role of community- and school-level factors on individual-level gang joining. We assess our research question by bringing together data from a statewide survey of more than 35,000 school youth who were enrolled in more than 250 schools and resided in more than 300 communities. We first use multilevel modeling techniques to examine the independent relationship of community and school on the likelihood of individual youth gang joining. Second, while controlling for individual-level risk and protective factors, we examine the simultaneous relationship of community- and school-level conditions on gang joining. Overall, the results of the present study suggest that both community- and school-level characteristics play a role in gang joining. The results of the cross-classified model suggests that community structural characteristics may serve as protection against gang joining. Individual-level risk and protective factors remain important for understanding gang joining. A variety of factors are related to gang joining. Though individual-level factors are important, community- and school-level factors are meaningful in the understanding of gang joining.     

Single‐item big five ratings in a social network design

To develop and validate an ultra‐short measure to assess the Big Five in social network designs, the unipolar items of the Ten‐Item Personality Inventory were adapted to create a bipolar single‐item scale (TIPI‐r), including a new Openness item. Reliability was examined in terms of the internal consistency and test–retest stability of self‐ratings and peer‐rating composites (trait reputations). Validity was examined by means of convergence between TIPI‐r and Big Five Inventory (BFI) scores, self‐peer agreement and projection (intra‐ individual correlation between self‐ and peer‐ratings). The psychometric quality of the TIPI‐r differed somewhat between scales and the different reliability and validity criteria. The high reliability of the peer‐rating composites motivates to use the TIPI‐r in future studies employing social network designs. Copyright © 2007 John Wiley & Sons, Ltd.     

气氛强度:理论基础及其研究框架

随着多层次理论的发展,气氛强度成为组织管理研究中受到关注的新概念。以多层次理论中的合成模型和文化强度为理论基础,研究者对气氛强度的前因变量及其作用开展了一系列的研究。本文在回顾气氛强度的理论基础和研究框架的基础上,提出未来研究首先应补充气氛强度前因变量的探索;并在不同行业和不同工作类型中去验证气氛强度的作用;最后应关注对气氛的概念界定,从而扩展对气氛强度的研究。     

儿童攻击和同伴关系的相关:20年研究的元分析

用多水平分析技术对近20年关于儿童攻击和同伴关系相关的研究结果进行的元分析显示,现有研究中关于攻击和同伴关系相关结果的变异程度比较大,总体来说攻击和同伴接受之间有负向相关关系,和同伴拒绝之间有正向相关关系。